Regarding office buildings or similar estates, where many people work or live in one construction of many floors, there is a constantly changing traffic of people wanting to change the vertical position. Elevator systems in such buildings are used by different number of passengers at different times of day.
For example, concerning the above mentioned office building, there will be a high volume of traffic in the morning, when people arrive for work, followed by a time when the elevator system is frequented by only few passengers during the morning. At noon there probably will be a rush hour when people go to lunch and return to their offices, followed again by a calm time during noon and a high traffic in the evening when people leave their offices and go home.
Passengers who are independent regarding their time of travel would be advantageously using the elevator at times of low traffic to reduce as well waiting time as also travel time. It would be of an advantage for them to know the best travel-time.
Methods known in the art, that are used to determine the travelling and waiting time are based on data about the actual allocated destinations of elevator cars and their actual position. The results of a processing of this data are shown for passengers on some screens most likely arranged in the lobby.
Disadvantages of the known methods are the small dataset, the non-optimal presentation of results, the slow updating of results, and the therefore resulting inability to provide optimal forecasts for future travels optimally presented to potential passengers.